TY - RPRT A1 - Clutton-Brock, Peter A1 - Rolnick, David A1 - Donti, Priya L. A1 - Kaack, Lynn T1 - Climate Change and AI. Recommendations for Government Action BT - Global Partnership on AI Report. In collaboration with Climate Change AI and the Centre for AI & Climate N2 - The report, Climate Change and AI: Recommendations for Government, highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses. Y1 - 2021 UR - https://www.gpai.ai/projects/responsible-ai/environment/climate-change-and-ai.pdf ER - TY - JOUR A1 - Kaack, Lynn A1 - Donti, Priya L. A1 - Strubell, Emma A1 - Kamiya, George A1 - Creutzig, Felix A1 - Rolnick, David T1 - Aligning artificial intelligence with climate change mitigation JF - Nature Climate Change N2 - There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. Y1 - 2022 U6 - https://doi.org/10.1038/s41558-022-01377-7 VL - 12 SP - 518 EP - 527 ER - TY - JOUR A1 - Kaack, Lynn A1 - Rolnick, David A1 - Donti, Priya L. A1 - Kochanski, Kelly A1 - Lacoste, Alexandre A1 - Sankaran, Kris A1 - Ross, Andrew S. A1 - Milojevic-Dupont, Nikola A1 - Jaques, Natasha A1 - Waldman-Brown, Anna A1 - Luccioni, Alexandra S. A1 - Maharaj, Tegan A1 - Sherwin, Evan D. A1 - Mukkavilli, Karthik A1 - Kording, Konrad P. A1 - Gomes, Carla P. A1 - Ng, Andrew Y. A1 - Hassabis, Demis A1 - Platt, John C. A1 - Creutzig, Felix A1 - Chayes, Jennifer A1 - Bengio, Yoshua T1 - Tackling Climate Change with Machine Learning JF - ACM Computing Surveys N2 - Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change. Y1 - 2022 UR - https://dl.acm.org/doi/10.1145/3485128 U6 - https://doi.org/10.1145/3485128 VL - 55 IS - 2 SP - 1 EP - 96 ER - TY - RPRT A1 - Yaakoubi, Yassine A1 - Donti, Priya L. A1 - Kaack, Lynn H. A1 - Rolnick, David A1 - Dunietz, Jesse A1 - Malik, Muneeb A1 - Afolabi, Temilola A1 - Kuhne, Paul A1 - Ndzimande, Onezwa A1 - Chinyamakobvu, Mutsa A1 - Gombakomba, Ruvimbo A1 - Chibvongodze, Vongai A1 - Leslie, Timothy A1 - Bjärby, Effuah A1 - Letuka, Teboho T1 - Grand Challenge Initiatives in AI for Climate & Nature: Landscape Assessment and Recommendations Y1 - 2024 UR - https://ccai-reports.s3.us-east-2.amazonaws.com/grand-challenge-initiatives-ai-climate-nature.pdf PB - Climate Change AI (CCAI) ER - TY - JOUR A1 - Kaack, Lynn A1 - Rolnick, David A1 - Reisch, Lucia A. A1 - Joppa, Lucas A1 - Howson, Peter A1 - Gil, Artur A1 - Alevizou, Panayiota A1 - Michaelidou, Nina A1 - Appiah-Campbell, Ruby A1 - Santarius, Tilman A1 - Köhler, Susanne A1 - Pizzol, Massimo A1 - Schweizer, Pia-Johanna A1 - Srinivasan, Dipti A1 - Kaack, Lynn A1 - Donti, Priya L. T1 - Digitizing a sustainable future JF - One Earth N2 - Digital technologies have a crucial role in facilitating transitions toward a sustainable future. Yet there remain challenges to overcome and pitfalls to avoid. This Voices asks: how do we leverage the digital transformation to successfully support a sustainability transition? Y1 - 2021 U6 - https://doi.org/10.1016/j.oneear.2021.05.012 VL - 4 IS - 6 SP - 768 EP - 771 ER - TY - RPRT A1 - Kaack, Lynn A1 - Donti, Priya A1 - Strubell, Emma A1 - Rolnick, David T1 - Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals N2 - With the increasing deployment of artificial intelligence (AI) technologies across society, it is important to understand in which ways AI may accelerate or impede climate progress, and how various stakeholders can guide those developments. On the one hand, AI can facilitate climate change mitigation and adaptation strategies within a variety of sectors, such as energy, manufacturing, agriculture, forestry, and disaster management. On the other hand, AI can also contribute to rising greenhouse gas emissions through applications that benefit high-emitting sectors or drive increases in consumer demand, as well as via energy use associated with AI itself. Here, we provide a brief overview of AI’s multi-faceted relationship with climate change, and recommend policy levers to align the use of AI with climate change mitigation and adaptation pathways. Y1 - 2020 UR - https://eu.boell.org/en/2020/12/03/artificial-intelligence-and-climate-change ER -